A Regression-optimization Framework for Sequential Decision-making

Abstract

This talk focuses on system-wide planning problems, wherein regression models are used to capture the dynamic behavior of various subcomponents. We model system dynamics using piecewise linear regression models, neural networks and random forests, and formulate the planning problem as a mixed-integer linear program that can additionally consume system and flow-based constraints. We demonstrate the use of this regression-optimization framework in generating policies that optimize system output, as well as in sequentially refining the policy trajectory by controlling for prediction error propagation.